ML Performance Evaluation Configuration via Supervised Learning
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Solution Overview
Problem
Existing machine learning systems face challenges in automatically configuring performance evaluation schemes, such as Holdout and k-fold validation, to determine the optimal configuration for given data sets and algorithms, leading to suboptimal model validation and selection.
Innovation Solution
A system comprising a processor and memory that includes a selection component for choosing performance evaluation metrics, a configuration component for configuring performance evaluation schemes using supervised learning, and an optimization component that adjusts the ratio of training to validation data set sizes to optimize algorithm accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated configuration of performance evaluation schemes is implemented, then model validation accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs self-configuration by automatically selecting performance evaluation schemes and optimizing their parameters based on the characteristics of the machine learning model and dataset, eliminating the need for manual expert intervention in the configuration process
Solution Approach 2:
The system automatically adjusts configuration parameters such as the number of folds in cross-validation, stratification settings, and evaluation metrics based on the specific characteristics of the model and data, transforming a static configuration process into a dynamic adaptive one
2Device complexity
If manual configuration of performance evaluation schemes is used, then system complexity is reduced, but model selection performance deteriorates
Solution Approach 1:
The patent replaces manual expert judgment and experience-based configuration with an automated computational system that uses algorithms to select and optimize performance evaluation schemes, substituting human mechanical processes with systematic automated procedures
3Ease of operation
If performance evaluation schemes are not optimized, then ease of operation is maintained, but generalization performance deteriorates
Solution Approach 1:
The system automatically optimizes performance evaluation configurations without requiring user intervention, making the optimization process as easy to operate as simply initiating the automated pipeline while achieving superior generalization performance
Data Source
AI summary
A system that provides a mathematical formulation for new problem of model validation and model selection in presence of test data feedback. The system comprises a memory that stores computer-executable components. A processor, operably coupled to the memory, executes the computer-executable components stored in the memory. A selection component selects a metric of performance evaluation accuracy; and a configuration component configures performance evaluation schemes for machine learning algorithms. A characterization component employs a supervised learning-based approach to characterize relationship between the configuration of the performance evaluation scheme and fidelity of performance estimates; and an optimization component that optimizes accuracy of the machine learning algorithms as a function of size of training data set relative to size of validation data set through selection of values associated with the configuration parameters.